The dataset used in this report is provided by the ESRC Consumer Data Research Centre. The csv files contain information about WiFi networks that are purposely made available to public members from June to July 2014 in London, comprising projection coordinates (easting and northing), geographic coordinates (latitude and longitude), number of public WiFi hotspots in one place and the postcode. Besides, the London boundary shapefile was downloaded from the UK Data Service.
However, the csv files containing London WiFi data are separated by the Local Authority District, so the first thing we need to do is using R to combine them into one single csv file.
setwd("E:/UCL/GIS/Assessment1/data/London_Wifi")
# Get the files names
fileNames <- list.files(pattern = "*.csv")
# Combile files
readFiles <- lapply(fileNames, function(x) read.csv(x, stringsAsFactors = F, header = T))
fileBind <- do.call(rbind, readFiles)
# Save combined file
setwd("E:/UCL/GIS/Assessment1/data")
write.csv(fileBind, "London_Wifi.csv")
wifi_all <- read.csv("London_Wifi.csv")
head(wifi_all)
## X pcd hotspots easting northing latitude longitude
## 1 1 E16AN 1 533414 181742 -0.07853589 51.51885
## 2 2 E17AA 1 533608 181326 -0.07589907 51.51507
## 3 3 E17BH 1 533479 181372 -0.07773963 51.51551
## 4 4 E17BS 2 533544 181365 -0.07680609 51.51543
## 5 5 E17DB 1 533511 181437 -0.07725414 51.51609
## 6 6 E17HP 1 533448 181666 -0.07807490 51.51816
Point Density Map is a raster map reveals where points are clustered in a given area by calculating the density of point features around each output raster cell. This report introduces two ways to make point density maps, one by the GUI-based software ArcGIS, and one by the opensource code-based software R.